Volume 32 Issue 5
Sep.  2023
Turn off MathJax
Article Contents
WU Xiaochun and WEN Xin, “Research on Health Stage Division of Switch Machine Based on Bray-Curtis Distance and Fisher Optimal Segmentation Method,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 955-962, 2023, doi: 10.23919/cje.2022.00.250
Citation: WU Xiaochun and WEN Xin, “Research on Health Stage Division of Switch Machine Based on Bray-Curtis Distance and Fisher Optimal Segmentation Method,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 955-962, 2023, doi: 10.23919/cje.2022.00.250

Research on Health Stage Division of Switch Machine Based on Bray-Curtis Distance and Fisher Optimal Segmentation Method

doi: 10.23919/cje.2022.00.250
Funds:  This work was supported by the Project Fund of China National Railway Group Co., Ltd. (N2022G012) and the Gansu Excellent Postgraduate Innovation Star Project (2022CXZX-615).
More Information
  • Author Bio:

    Xiaochun WU was born in 1973. She is an Professor of Lanzhou Jiaotong University, and Canadian UBC Visiting Scholar. Her main research interests focus on signal processing and train operation control. (Email: 369038806@qq.com)

    Xin WEN was born in 1998. She is currently pursuing the postgraduate in Lanzhou Jiaotong University, majored in traffic information engineering and control. Her research interests include the research and prediction of health status assessment of switch machine. (Email: 1793807303@qq.com)

  • Received Date: 2022-07-31
  • Accepted Date: 2022-11-16
  • Available Online: 2023-01-07
  • Publish Date: 2023-09-05
  • In order to reasonably and accurately evaluate the health status of the switch machine, a health stage division method of switch machine combining Bray-Curtis distance and Fisher optimal segmentation is proposed. First, the power curve of switch machine is divided into five sections, and eight time-domain characteristic parameters of each section are extracted. Second, the characteristic parameters with the largest correlation between fifteen dimensions and state of the switch machine are selected by using the Holder coefficient method as the input of Bray-Curtis distance algorithm, using Bray-Curtis distance to calculate health index (HI), which represents health state of switch machine. Finally, HI curve is divided by Fisher optimal segmentation method, and the optimal number of health stages of switch machine is determined to be three, and HI interval and threshold of each health stage are obtained. The effectiveness of this method is verified by 4382 sets of on-site switch machine data experiments. The experimental results show that the health index curve calculated by Bray-Curtis distance can accurately represent the health status of the switch machine. Compared with Frechet distance and European distance, this method has better performance in tendency, robustness, and runtime. Combining with Fisher optimal segmentation method, it can reasonably and effectively divide the health stage of the switch machine, providing some support for the on-site judgment of the health status of the switch machine.
  • loading
  • [1]
    Z. W. Zhong, “Research on methods of health condition assessment and prediction of railway turnout,” Ph.D. Thesis, Beijing Jiaotong University, Beijing, China, 2019, pp.4–27. (in Chinese).
    [2]
    L. M. Gao, Q. Y. Xu, F. Li, et al., “Research on degradation state of turnout equipment based on SOM-BP hybrid neural network,” China Railway Science, vol.41, no.3, pp.50–58, 2020. (in Chinese) doi: 10.3969/j.issn.1001-4632.2020.03.06
    [3]
    Y. X. Li, “Research on degradation model of turnout switch based on neural network,” Master Thesis, Beijing Jiaotong University, Beijing, China, 2020, pp.1–34. (in Chinese)
    [4]
    A. Kampczyk and K. Dybeł, “The fundamental approach of the digital twin application in railway turnouts with innovative monitoring of weather conditions,” Sensors, vol.21, no.17, article no.5757, 2021. doi: 10.3390/s21175757
    [5]
    W. Z. Cheng, H. D. Wang, and Y. Liang, “Research on fault analysis and diagnosis monitoring system for railway turnout switch machine,” China Railway, no.7, pp.43–47, 2018. (in Chinese) doi: 10.19549/j.issn.1001-683x.2018.07.043
    [6]
    K. Zhang, “The railway turnout fault diagnosis algorithm based on BP neural network,” in Proceedings of 2014 IEEE International Conference on Control Science and Systems Engineering, Yantai, China, pp.135–138, 2014.
    [7]
    J. Lee, H. Choi, D. Park, et al., “Fault detection and diagnosis of railway point machines by sound analysis,” Sensors, vol.16, no.4, article no.549, 2016. doi: 10.3390/s16040549
    [8]
    S. D. Bemment, R. M. Goodall, R. Dixon, et al., “Improving the reliability and availability of railway track switching by analysing historical failure data and introducing functionally redundant subsystems,” Proceedings of the Institution of Mechanical Engineers, Part F:Journal of Rail and Rapid Transit, vol.232, no.5, pp.1407–1424, 2018. doi: 10.1177/0954409717727879
    [9]
    X. C. Wu and X. Chu, “Research on division of degradation stage of turnout equipment based on wavelet packet decomposition and GG fuzzy clustering,” Journal of the China Railway Society, vol.44, no.1, pp.79–85, 2022. doi: 10.3969/j.issn.1001-8360.2022.01.011
    [10]
    X. D. Qin, “ZYJ7 type electro-hydraulic turnout daily maintenance and troubleshooting points analysis,” Municipal Engineering, vol.7, no.1, pp.110–124, 2022.
    [11]
    H. X. Lin and Y. Dong, “Signal centralized monitoring system,” in Principles and Engineering Applications of Signal Centralized Monitoring System, China Railway Publishing House, Beijing, China, pp.20–58, 2015 (in Chinese).
    [12]
    Editorial Board, “ZYJ7 switch machine”, in Typical Case Analysis of Railway Signal Centralized Monitoring, China Railway Publishing House, Beijing, pp.20–67, 2020 (in Chinese).
    [13]
    H. H. Wang and X. F. Shen, “New intra-pulse feature extraction approach of radar emitter signals,” Systems Engineering and Electronics, vol.31, no.4, pp.809–811, 2009. (in Chinese) doi: 10.3321/j.issn:1001-506X.2009.04.019
    [14]
    C. C. Xu, Q. S. Zhou, J. Y. Zhang, et al., “Radar emitter recognition based on ambiguity function features with derivative constraint on smoothing,” Acta Electronica Sinica, vol.46, no.7, pp.1663–1668, 2018. (in Chinese) doi: 10.3969/j.issn.0372-2112.2018.07.018
    [15]
    Y. J. Yuan, S. W. Chen, Z. X. Liu, et al., “Radar signal sorting method based on the symmetric Holder coefficients of high-order spectrum,” Journal of Signal Processing, vol.36, no.10, pp.1775–1783, 2020. (in Chinese) doi: 10.16798/j.issn.1003-0530.2020.10.018
    [16]
    Z. H. Zhao, L. H. Li, S. P. Yang, et al., “An unsupervised bearing health indicator and early fault detection method,” China Mechanical Engineering, vol.33, no.10, pp.1234–1243, 2022. (in Chinese) doi: 10.3969/j.issn.1004-132X.2022.10.013
    [17]
    X. X. Hu, R. Niu, and T. Tang, “Pre-processing of metro signaling equipment fault text based on fusion of lexical domain and semantic domain,” Journal of the China Railway Society, vol.43, no.2, pp.78–85, 2021. (in Chinese) doi: 10.3969/j.issn.1001-8360.2021.02.010
    [18]
    G. K. Hu and Q. T. Zhang, “An applicability comparison between two similarity coefficients to biological assemblage analysis,” Transactions of Oceanology and Limnology, no.4, pp.140–145, 2019. (in Chinese) doi: 10.13984/j.cnki.cn37-1141.2019.04.017
    [19]
    L. Liu, H. Wang, C. C. Lin, et al., “Vegetation and community changes of elm (Ulmus pumila) woodlands in northeastern China in 1983–2011,” Chinese Geographical Science, vol.23, no.3, pp.321–330, 2013. doi: 10.1007/s11769-013-0607-8
    [20]
    X. Zhang, “Analysis of health management of high speed railway speed-up switch,” Railway Signalling & Communication Engineering, vol.16, no.2, pp.80–83, 2019. (in Chinese) doi: 10.3969/j.issn.1673-4440.2019.02.019
    [21]
    Z. F. Wang, J. Q. Zhen, F. Z. Zhu, et al., “Quaternion kernel fisher discriminant analysis for feature-level multimodal biometric recognition,” Chinese Journal of Electronics, vol.29, no.6, pp.1085–1092, 2020. doi: 10.1049/cje.2020.09.009
    [22]
    H. J. Wang and P. L. Wu, “Flood season division based on fisher optimal partition method,” Yellow River, vol.37, no.8, pp.30–34, 2015. (in Chinese) doi: 10.3969/j.issn.1000-1379.2015.08.009
    [23]
    Y. Cao, P. Li, and Y. Z. Zhang, “Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing,” Future Generation Computer Systems, vol.88, pp.279–283, 2018. doi: 10.1016/j.future.2018.05.038
    [24]
    F. Gao, J. Liu, X. G. Yang, et al., “Study on optimization of thermal key points for machine tools based on Fisher optimal segmentation method,” Chinese Journal of Scientific Instrument, vol.34, no.5, pp.1070–1075, 2013. (in Chinese) doi: 10.3969/j.issn.0254-3087.2013.05.017
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(7)

    Article Metrics

    Article views (356) PDF downloads(48) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return